Experiment!: Planning, Implementing and Interpreting

7.2 Populations and Samples

A research team may want to investigate how a certain diet affects the blood pressure of healthy people. It would, of course, be impractical to try to convince every healthy person in the world to participate in this study. Instead, they choose to work with a group of people. The group should be representative for all healthy people, so they must be careful to choose the people randomly. For example, they should not choose people working in a single profession, practicing the same sport, or having the same age, since people in such groups can be expected to respond more similarly to the diet than the whole group of healthy people. After testing their selected group, the researchers use statistical techniques to infer something about how the whole group of healthy people responds to the diet.

In this example, the whole group of healthy people is called the population and the selected group is called a sample. The population always consists of the complete set of possible observations and, in this case, it includes all people that have ever lived and those who are not yet born. This is because we are interested in treating any possible person with the diet, not only those who live today. Entire populations are most often so large that it is practically impossible to work with them. For practical reasons, we always work with subsets of populations – samples. To be useful to our investigations they must be random samples. This means that the sampling must ...